Senior Data Engineer

Location: New York, NY / Chicago, IL

Work Model: Hybrid

Experience: 8+ yearsAbout the RoleWe are looking for an experienced AVP, Data Engineer to design, build, and support modern data platforms and transformation pipelines. The role will focus on ingesting and transforming replicated source data into trusted, consumable data products for reporting, analytics, and downstream business users. The ideal candidate will have strong expertise in data modeling, SQL, Python, dbt, and data pipeline development, along with experience working with modern analytical data platforms and workflow orchestration tools. This is a hybrid opportunity, with the candidate expected to be within a commutable distance of the New York or Chicago office.

Key Responsibilities: Translate business requirements and data mapping documents into technical designs. Develop and optimize SQL-based data transformations using dbt. Build and maintain data pipelines using Dagster or similar orchestration tools. Identify, troubleshoot, and resolve data quality and SQL-related issues. Debug and troubleshoot issues identified during testing and production. Monitor data pipelines and ensure reliable delivery of data products. Communicate project status, technical issues, risks, and blockers effectively. Identify opportunities for continuous improvement across data engineering processes. Mentor junior engineers and contribute to technical best practices. Participate in code reviews and promote quality engineering standards.

Required Qualifications: Bachelor's degree in Computer Science or a related field, or equivalent professional experience.8+ years of Data Engineering experience designing, developing, and supporting enterprise data platforms, ingestion frameworks, and transformation pipelines. Strong hands-on expertise in:

• SQLPythondbtExperience developing and optimizing large-scale ETL/ELT solutions.
• Hands-on experience with Dagster, Airflow, or similar workflow orchestration tools.
• Experience building data ingestion and replication pipelines from:
• PostgreSQLOracleNoSQL sourcesFile-based data feedsExperience working with Snowflake, Databricks, or comparable modern data platforms.
• Strong understanding of data modeling concepts, including:
• Dimensional ModelingMedallion ArchitectureData VaultHighly relational data structuresExperience transforming complex business data into reliable datasets for reporting, analytics, and downstream users.
• Strong understanding of data engineering best practices, including:
• TestingCode reviewsGit/version controlCI/CDPreferred QualificationsExperience with distributed data processing technologies.
• Exposure to Kubernetes and modern cloud-based data ecosystems.
• Experience mentoring engineers and conducting technical/code reviews.
• Experience working in a player-coach capacity, balancing hands-on development with technAVP, Data Engineer

Location: New York, NY / Chicago, IL

Work Model: Hybrid

Experience: 8+ yearsAbout the RoleWe are looking for an experienced AVP, Data Engineer to design, build, and support modern data platforms and transformation pipelines. The role will focus on ingesting and transforming replicated source data into trusted, consumable data products for reporting, analytics, and downstream business users. The ideal candidate will have strong expertise in data modeling, SQL, Python, dbt, and data pipeline development, along with experience working with modern analytical data platforms and workflow orchestration tools. This is a hybrid opportunity, with the candidate expected to be within a commutable distance of the New York or Chicago office.

Key Responsibilities: Translate business requirements and data mapping documents into technical designs. Develop and optimize SQL-based data transformations using dbt. Build and maintain data pipelines using Dagster or similar orchestration tools. Identify, troubleshoot, and resolve data quality and SQL-related issues. Debug and troubleshoot issues identified during testing and production. Monitor data pipelines and ensure reliable delivery of data products. Communicate project status, technical issues, risks, and blockers effectively. Identify opportunities for continuous improvement across data engineering processes. Mentor junior engineers and contribute to technical best practices. Participate in code reviews and promote quality engineering standards.

Required Qualifications: Bachelor's degree in Computer Science or a related field, or equivalent professional experience.8+ years of Data Engineering experience designing, developing, and supporting enterprise data platforms, ingestion frameworks, and transformation pipelines. Strong hands-on expertise in:

• SQLPythondbtExperience developing and optimizing large-scale ETL/ELT solutions.
• Hands-on experience with Dagster, Airflow, or similar workflow orchestration tools.
• Experience building data ingestion and replication pipelines from:
• PostgreSQLOracleNoSQL sourcesFile-based data feedsExperience working with Snowflake, Databricks, or comparable modern data platforms.
• Strong understanding of data modeling concepts, including:
• Dimensional ModelingMedallion ArchitectureData VaultHighly relational data structuresExperience transforming complex business data into reliable datasets for reporting, analytics, and downstream users.
• Strong understanding of data engineering best practices, including:
• TestingCode reviewsGit/version controlCI/CDPreferred QualificationsExperience with distributed data processing technologies.
• Exposure to Kubernetes and modern cloud-based data ecosystems.
• Experience mentoring engineers and conducting technical/code reviews.
• Experience working in a player-coach capacity, balancing hands-on development with technical leadership.
• Experience in Property & Casualty Insurance, InsurTech, Financial Services, or a similar regulated industry.
• Ability to work effectively in a fast-paced, rapidly changing environment.ical leadership.
• Experience in Property & Casualty Insurance, InsurTech, Financial Services, or a similar regulated industry.
• Ability to work effectively in a fast-paced, rapidly changing environment.